{% block head %} Pandas Profiling Report {% endblock %} {% block content %}

Overview

Dataset statistics

Number of variables 11
Number of observations 177733
Missing cells 81
Missing cells (%) < 0.1%
Duplicate rows 2
Duplicate rows (%) < 0.1%
Total size in memory 14.9 MiB
Average record size in memory 88.0 B

Variable types

Numeric 7
Categorical 4

Alerts

Dataset has 2 (< 0.1%) duplicate rows Duplicates
Building address has a high cardinality: 21278 distinct values High cardinality
Location has a high cardinality: 19084 distinct values High cardinality
Block ID is highly overall correlated with Property ID and 4 other fields High correlation
Property ID is highly overall correlated with Block ID and 3 other fields High correlation
Base property ID is highly overall correlated with Block ID and 3 other fields High correlation
x coordinate is highly overall correlated with Block ID and 4 other fields High correlation
y coordinate is highly overall correlated with Block ID and 4 other fields High correlation
CLUE small area is highly overall correlated with Block ID and 3 other fields High correlation
Dwelling type is highly overall correlated with CLUE small area High correlation

Reproduction

Analysis started 2022-11-29 10:27:14.699480
Analysis finished 2022-11-29 10:27:34.448483
Duration 19.75 seconds
Software version pandas-profiling vv3.5.0
Download configuration config.json

Variables

Census year
Real number (ℝ)

Distinct 19
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 2011.5408
Minimum 2002
Maximum 2020
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 MiB
2022-11-29T21:27:34.514510 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 2002
5-th percentile 2003
Q1 2007
median 2012
Q3 2016
95-th percentile 2020
Maximum 2020
Range 18
Interquartile range (IQR) 9

Descriptive statistics

Standard deviation 5.3669328
Coefficient of variation (CV) 0.0026680705
Kurtosis -1.1403448
Mean 2011.5408
Median Absolute Deviation (MAD) 4
Skewness -0.1163969
Sum 3.5751719 × 108
Variance 28.803967
Monotonicity Increasing
2022-11-29T21:27:34.622482 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
Value Count Frequency (%)
2020 10402
 
5.9%
2019 10308
 
5.8%
2018 10268
 
5.8%
2017 10177
 
5.7%
2016 10154
 
5.7%
2015 10096
 
5.7%
2014 10064
 
5.7%
2013 9939
 
5.6%
2012 9910
 
5.6%
2011 9792
 
5.5%
Other values (9) 76623
43.1%
Value Count Frequency (%)
2002 7510
4.2%
2003 7657
4.3%
2004 7890
4.4%
2005 7998
4.5%
2006 8048
4.5%
2007 8426
4.7%
2008 9593
5.4%
2009 9740
5.5%
2010 9761
5.5%
2011 9792
5.5%
Value Count Frequency (%)
2020 10402
5.9%
2019 10308
5.8%
2018 10268
5.8%
2017 10177
5.7%
2016 10154
5.7%
2015 10096
5.7%
2014 10064
5.7%
2013 9939
5.6%
2012 9910
5.6%
2011 9792
5.5%

Block ID
Real number (ℝ)

Distinct 465
Distinct (%) 0.3%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 676.8354
Minimum 1
Maximum 2547
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 MiB
2022-11-29T21:27:34.758513 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 206
Q1 318
median 521
Q3 854
95-th percentile 2520
Maximum 2547
Range 2546
Interquartile range (IQR) 536

Descriptive statistics

Standard deviation 639.24944
Coefficient of variation (CV) 0.94446809
Kurtosis 3.5826392
Mean 676.8354
Median Absolute Deviation (MAD) 215
Skewness 2.1439976
Sum 1.2029599 × 108
Variance 408639.85
Monotonicity Not monotonic
2022-11-29T21:27:34.901481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
928 2952
 
1.7%
860 2495
 
1.4%
862 2410
 
1.4%
306 2166
 
1.2%
861 2109
 
1.2%
922 1978
 
1.1%
352 1846
 
1.0%
857 1714
 
1.0%
527 1699
 
1.0%
301 1556
 
0.9%
Other values (455) 156808
88.2%
Value Count Frequency (%)
1 26
 
< 0.1%
11 78
< 0.1%
12 82
< 0.1%
13 38
 
< 0.1%
14 166
0.1%
15 73
< 0.1%
16 83
< 0.1%
17 153
0.1%
18 72
< 0.1%
23 38
 
< 0.1%
Value Count Frequency (%)
2547 223
0.1%
2546 507
0.3%
2544 174
 
0.1%
2543 480
0.3%
2542 455
0.3%
2541 371
0.2%
2539 305
0.2%
2538 243
0.1%
2537 488
0.3%
2536 489
0.3%

Property ID
Real number (ℝ)

Distinct 10913
Distinct (%) 6.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 220065.17
Minimum 1
Maximum 707415
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 MiB
2022-11-29T21:27:35.045482 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 100673
Q1 103585
median 107275
Q3 111252
95-th percentile 616690
Maximum 707415
Range 707414
Interquartile range (IQR) 7667

Descriptive statistics

Standard deviation 207338.32
Coefficient of variation (CV) 0.94216779
Kurtosis -0.35156642
Mean 220065.17
Median Absolute Deviation (MAD) 3807
Skewness 1.2712654
Sum 3.9112843 × 1010
Variance 4.2989178 × 1010
Monotonicity Not monotonic
2022-11-29T21:27:35.184510 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
105151 38
 
< 0.1%
106623 38
 
< 0.1%
108496 38
 
< 0.1%
107367 38
 
< 0.1%
109638 37
 
< 0.1%
109500 36
 
< 0.1%
581559 34
 
< 0.1%
105803 32
 
< 0.1%
105670 30
 
< 0.1%
101281 27
 
< 0.1%
Other values (10903) 177385
99.8%
Value Count Frequency (%)
1 4
 
< 0.1%
50298 3
 
< 0.1%
100001 7
 
< 0.1%
100004 19
< 0.1%
100005 5
 
< 0.1%
100007 1
 
< 0.1%
100011 9
< 0.1%
100013 2
 
< 0.1%
100014 19
< 0.1%
100015 19
< 0.1%
Value Count Frequency (%)
707415 1
< 0.1%
707414 1
< 0.1%
707413 1
< 0.1%
707412 1
< 0.1%
707372 1
< 0.1%
705841 1
< 0.1%
705840 1
< 0.1%
704768 1
< 0.1%
704760 1
< 0.1%
704480 1
< 0.1%

Base property ID
Real number (ℝ)

Distinct 10663
Distinct (%) 6.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 217701.47
Minimum 100001
Maximum 707415
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 MiB
2022-11-29T21:27:35.335479 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 100001
5-th percentile 100673
Q1 103562
median 107219
Q3 111115
95-th percentile 616566
Maximum 707415
Range 607414
Interquartile range (IQR) 7553

Descriptive statistics

Standard deviation 205816.47
Coefficient of variation (CV) 0.94540689
Kurtosis -0.27114753
Mean 217701.47
Median Absolute Deviation (MAD) 3760
Skewness 1.3026614
Sum 3.8692735 × 1010
Variance 4.2360418 × 1010
Monotonicity Not monotonic
2022-11-29T21:27:35.471481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
102492 397
 
0.2%
106250 191
 
0.1%
107110 190
 
0.1%
106681 180
 
0.1%
102697 173
 
0.1%
108881 171
 
0.1%
105330 152
 
0.1%
111207 130
 
0.1%
109643 115
 
0.1%
107153 105
 
0.1%
Other values (10653) 175929
99.0%
Value Count Frequency (%)
100001 7
 
< 0.1%
100004 19
< 0.1%
100005 5
 
< 0.1%
100007 1
 
< 0.1%
100011 9
< 0.1%
100013 2
 
< 0.1%
100014 19
< 0.1%
100015 19
< 0.1%
100016 19
< 0.1%
100017 19
< 0.1%
Value Count Frequency (%)
707415 1
< 0.1%
707414 1
< 0.1%
707413 1
< 0.1%
707412 1
< 0.1%
704768 1
< 0.1%
704760 1
< 0.1%
703803 1
< 0.1%
701500 2
< 0.1%
701403 1
< 0.1%
701402 1
< 0.1%

Building address
Categorical

Distinct 21278
Distinct (%) 12.0%
Missing 0
Missing (%) 0.0%
Memory size 1.4 MiB
47-51 Little Palmerston Street CARLTON 3053
 
90
130 Kensington Road KENSINGTON 3031
 
85
570 Lygon Street CARLTON 3053
 
72
640 Swanston Street CARLTON 3053
 
58
52-54 Canning Street CARLTON 3053
 
54
Other values (21273)
177374 

Length

Max length 86
Median length 77
Mean length 35.391239
Min length 25

Characters and Unicode

Total characters 6290191
Distinct characters 69
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 6506 ?
Unique (%) 3.7%

Sample

1st row 517-537 Flinders Lane MELBOURNE 3000
2nd row 547-555 Flinders Lane MELBOURNE 3000
3rd row 492-500 Flinders Street MELBOURNE 3000
4th row 1-13 William Street MELBOURNE 3000
5th row 25-27 Highlander Lane MELBOURNE 3000

Common Values

Value Count Frequency (%)
47-51 Little Palmerston Street CARLTON 3053 90
 
0.1%
130 Kensington Road KENSINGTON 3031 85
 
< 0.1%
570 Lygon Street CARLTON 3053 72
 
< 0.1%
640 Swanston Street CARLTON 3053 58
 
< 0.1%
52-54 Canning Street CARLTON 3053 54
 
< 0.1%
250 St Kilda Road SOUTHBANK 3006 51
 
< 0.1%
23 Franklin Place WEST MELBOURNE 3003 46
 
< 0.1%
82 Kensington Road KENSINGTON 3031 45
 
< 0.1%
2 McCracken Street KENSINGTON 3031 44
 
< 0.1%
28 Erskine Street NORTH MELBOURNE 3051 36
 
< 0.1%
Other values (21268) 177152
99.7%

Length

2022-11-29T21:27:35.642483 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
street 130714
 
13.3%
melbourne 67107
 
6.8%
kensington 51867
 
5.3%
3031 50069
 
5.1%
north 37362
 
3.8%
3051 36703
 
3.7%
carlton 30573
 
3.1%
3053 29776
 
3.0%
south 16031
 
1.6%
yarra 14385
 
1.5%
Other values (3128) 520762
52.9%

Most occurring characters

Value Count Frequency (%)
807616
 
12.8%
e 409861
 
6.5%
t 334412
 
5.3%
3 323665
 
5.1%
N 292136
 
4.6%
r 247549
 
3.9%
S 238659
 
3.8%
0 238207
 
3.8%
E 229739
 
3.7%
1 214728
 
3.4%
Other values (59) 2953619
47.0%

Most occurring categories

Value Count Frequency (%)
Uppercase Letter 2297618
36.5%
Lowercase Letter 1907979
30.3%
Decimal Number 1224568
19.5%
Space Separator 807616
 
12.8%
Dash Punctuation 51308
 
0.8%
Other Punctuation 1102
 
< 0.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 409861
21.5%
t 334412
17.5%
r 247549
13.0%
a 144156
 
7.6%
n 117851
 
6.2%
o 108541
 
5.7%
l 86047
 
4.5%
i 70847
 
3.7%
s 56697
 
3.0%
d 53591
 
2.8%
Other values (16) 278427
14.6%
Uppercase Letter
Value Count Frequency (%)
N 292136
12.7%
S 238659
10.4%
E 229739
10.0%
O 205131
 
8.9%
R 196735
 
8.6%
T 164269
 
7.1%
L 141292
 
6.1%
A 101302
 
4.4%
M 87993
 
3.8%
U 82812
 
3.6%
Other values (16) 557550
24.3%
Decimal Number
Value Count Frequency (%)
3 323665
26.4%
0 238207
19.5%
1 214728
17.5%
5 127374
 
10.4%
2 95256
 
7.8%
4 69361
 
5.7%
6 44361
 
3.6%
8 38921
 
3.2%
7 37115
 
3.0%
9 35580
 
2.9%
Other Punctuation
Value Count Frequency (%)
' 1016
92.2%
, 68
 
6.2%
/ 8
 
0.7%
. 6
 
0.5%
& 4
 
0.4%
Space Separator
Value Count Frequency (%)
807616
100.0%
Dash Punctuation
Value Count Frequency (%)
- 51308
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 4205597
66.9%
Common 2084594
33.1%

Most frequent character per script

Latin
Value Count Frequency (%)
e 409861
 
9.7%
t 334412
 
8.0%
N 292136
 
6.9%
r 247549
 
5.9%
S 238659
 
5.7%
E 229739
 
5.5%
O 205131
 
4.9%
R 196735
 
4.7%
T 164269
 
3.9%
a 144156
 
3.4%
Other values (42) 1742950
41.4%
Common
Value Count Frequency (%)
807616
38.7%
3 323665
15.5%
0 238207
 
11.4%
1 214728
 
10.3%
5 127374
 
6.1%
2 95256
 
4.6%
4 69361
 
3.3%
- 51308
 
2.5%
6 44361
 
2.1%
8 38921
 
1.9%
Other values (7) 73797
 
3.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 6290191
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
807616
 
12.8%
e 409861
 
6.5%
t 334412
 
5.3%
3 323665
 
5.1%
N 292136
 
4.6%
r 247549
 
3.9%
S 238659
 
3.8%
0 238207
 
3.8%
E 229739
 
3.7%
1 214728
 
3.4%
Other values (59) 2953619
47.0%

CLUE small area
Categorical

Distinct 13
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.4 MiB
Kensington
50069 
North Melbourne
36799 
Carlton
29925 
South Yarra
14377 
East Melbourne
13154 
Other values (8)
33409 

Length

Max length 28
Median length 27
Mean length 12.180113
Min length 7

Characters and Unicode

Total characters 2164808
Distinct characters 34
Distinct categories 5 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Melbourne (CBD)
2nd row Melbourne (CBD)
3rd row Melbourne (CBD)
4th row Melbourne (CBD)
5th row Melbourne (CBD)

Common Values

Value Count Frequency (%)
Kensington 50069
28.2%
North Melbourne 36799
20.7%
Carlton 29925
16.8%
South Yarra 14377
 
8.1%
East Melbourne 13154
 
7.4%
Parkville 13137
 
7.4%
West Melbourne (Residential) 11629
 
6.5%
Melbourne (CBD) 5841
 
3.3%
Docklands 1494
 
0.8%
Southbank 992
 
0.6%
Other values (3) 316
 
0.2%

Length

2022-11-29T21:27:35.774479 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
melbourne 67739
25.0%
kensington 50069
18.4%
north 36799
13.6%
carlton 29925
11.0%
south 14377
 
5.3%
yarra 14377
 
5.3%
east 13154
 
4.8%
parkville 13137
 
4.8%
west 11632
 
4.3%
residential 11629
 
4.3%
Other values (6) 8643
 
3.2%

Most occurring characters

Value Count Frequency (%)
n 262283
12.1%
e 234162
 
10.8%
o 201414
 
9.3%
r 176670
 
8.2%
t 168599
 
7.8%
l 137064
 
6.3%
a 99382
 
4.6%
93748
 
4.3%
s 87981
 
4.1%
i 86761
 
4.0%
Other values (24) 616744
28.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1752363
80.9%
Uppercase Letter 283163
 
13.1%
Space Separator 93748
 
4.3%
Close Punctuation 17767
 
0.8%
Open Punctuation 17767
 
0.8%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
n 262283
15.0%
e 234162
13.4%
o 201414
11.5%
r 176670
10.1%
t 168599
9.6%
l 137064
7.8%
a 99382
 
5.7%
s 87981
 
5.0%
i 86761
 
5.0%
u 83111
 
4.7%
Other values (8) 214936
12.3%
Uppercase Letter
Value Count Frequency (%)
M 67739
23.9%
K 50069
17.7%
N 36799
13.0%
C 35766
12.6%
S 15369
 
5.4%
Y 14377
 
5.1%
P 13156
 
4.6%
E 13154
 
4.6%
R 11923
 
4.2%
W 11632
 
4.1%
Other values (3) 13179
 
4.7%
Space Separator
Value Count Frequency (%)
93748
100.0%
Close Punctuation
Value Count Frequency (%)
) 17767
100.0%
Open Punctuation
Value Count Frequency (%)
( 17767
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2035526
94.0%
Common 129282
 
6.0%

Most frequent character per script

Latin
Value Count Frequency (%)
n 262283
12.9%
e 234162
11.5%
o 201414
9.9%
r 176670
 
8.7%
t 168599
 
8.3%
l 137064
 
6.7%
a 99382
 
4.9%
s 87981
 
4.3%
i 86761
 
4.3%
u 83111
 
4.1%
Other values (21) 498099
24.5%
Common
Value Count Frequency (%)
93748
72.5%
) 17767
 
13.7%
( 17767
 
13.7%

Most occurring blocks

Value Count Frequency (%)
ASCII 2164808
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
n 262283
12.1%
e 234162
 
10.8%
o 201414
 
9.3%
r 176670
 
8.2%
t 168599
 
7.8%
l 137064
 
6.3%
a 99382
 
4.6%
93748
 
4.3%
s 87981
 
4.1%
i 86761
 
4.0%
Other values (24) 616744
28.5%

Dwelling type
Categorical

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.4 MiB
House/Townhouse
148371 
Residential Apartments
28516 
Student Apartments
 
846

Length

Max length 22
Median length 15
Mean length 16.13738
Min length 15

Characters and Unicode

Total characters 2868145
Distinct characters 22
Distinct categories 4 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Residential Apartments
2nd row Residential Apartments
3rd row Residential Apartments
4th row Residential Apartments
5th row House/Townhouse

Common Values

Value Count Frequency (%)
House/Townhouse 148371
83.5%
Residential Apartments 28516
 
16.0%
Student Apartments 846
 
0.5%

Length

2022-11-29T21:27:35.885481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T21:27:36.018510 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
house/townhouse 148371
71.6%
apartments 29362
 
14.2%
residential 28516
 
13.8%
student 846
 
0.4%

Most occurring characters

Value Count Frequency (%)
o 445113
15.5%
e 383982
13.4%
s 354620
12.4%
u 297588
10.4%
n 207095
7.2%
H 148371
 
5.2%
/ 148371
 
5.2%
T 148371
 
5.2%
w 148371
 
5.2%
h 148371
 
5.2%
Other values (12) 437892
15.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 2334946
81.4%
Uppercase Letter 355466
 
12.4%
Other Punctuation 148371
 
5.2%
Space Separator 29362
 
1.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
o 445113
19.1%
e 383982
16.4%
s 354620
15.2%
u 297588
12.7%
n 207095
8.9%
w 148371
 
6.4%
h 148371
 
6.4%
t 88932
 
3.8%
a 57878
 
2.5%
i 57032
 
2.4%
Other values (5) 145964
 
6.3%
Uppercase Letter
Value Count Frequency (%)
H 148371
41.7%
T 148371
41.7%
A 29362
 
8.3%
R 28516
 
8.0%
S 846
 
0.2%
Other Punctuation
Value Count Frequency (%)
/ 148371
100.0%
Space Separator
Value Count Frequency (%)
29362
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2690412
93.8%
Common 177733
 
6.2%

Most frequent character per script

Latin
Value Count Frequency (%)
o 445113
16.5%
e 383982
14.3%
s 354620
13.2%
u 297588
11.1%
n 207095
7.7%
H 148371
 
5.5%
T 148371
 
5.5%
w 148371
 
5.5%
h 148371
 
5.5%
t 88932
 
3.3%
Other values (10) 319598
11.9%
Common
Value Count Frequency (%)
/ 148371
83.5%
29362
 
16.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 2868145
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
o 445113
15.5%
e 383982
13.4%
s 354620
12.4%
u 297588
10.4%
n 207095
7.2%
H 148371
 
5.2%
/ 148371
 
5.2%
T 148371
 
5.2%
w 148371
 
5.2%
h 148371
 
5.2%
Other values (12) 437892
15.3%

Dwelling number
Real number (ℝ)

Distinct 318
Distinct (%) 0.2%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 6.1188018
Minimum 1
Maximum 1045
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 MiB
2022-11-29T21:27:36.148511 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 1
Q1 1
median 1
Q3 1
95-th percentile 18
Maximum 1045
Range 1044
Interquartile range (IQR) 0

Descriptive statistics

Standard deviation 29.626981
Coefficient of variation (CV) 4.8419579
Kurtosis 166.3606
Mean 6.1188018
Median Absolute Deviation (MAD) 0
Skewness 11.049656
Sum 1087513
Variance 877.75799
Monotonicity Not monotonic
2022-11-29T21:27:36.290481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
1 148204
83.4%
2 4848
 
2.7%
4 2829
 
1.6%
6 2305
 
1.3%
3 2200
 
1.2%
12 1562
 
0.9%
8 1380
 
0.8%
9 978
 
0.6%
5 976
 
0.5%
18 761
 
0.4%
Other values (308) 11690
 
6.6%
Value Count Frequency (%)
1 148204
83.4%
2 4848
 
2.7%
3 2200
 
1.2%
4 2829
 
1.6%
5 976
 
0.5%
6 2305
 
1.3%
7 649
 
0.4%
8 1380
 
0.8%
9 978
 
0.6%
10 492
 
0.3%
Value Count Frequency (%)
1045 1
 
< 0.1%
952 1
 
< 0.1%
814 2
 
< 0.1%
806 1
 
< 0.1%
805 1
 
< 0.1%
700 6
< 0.1%
661 4
< 0.1%
659 2
 
< 0.1%
643 2
 
< 0.1%
641 2
 
< 0.1%

x coordinate
Real number (ℝ)

Distinct 5384
Distinct (%) 3.0%
Missing 27
Missing (%) < 0.1%
Infinite 0
Infinite (%) 0.0%
Mean 144.9518
Minimum 144.9063
Maximum 144.9908
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 MiB
2022-11-29T21:27:36.437509 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 144.9063
5-th percentile 144.9203
Q1 144.9323
median 144.9494
Q3 144.9706
95-th percentile 144.9868
Maximum 144.9908
Range 0.0845
Interquartile range (IQR) 0.0383

Descriptive statistics

Standard deviation 0.02120098
Coefficient of variation (CV) 0.00014626228
Kurtosis -1.1087143
Mean 144.9518
Median Absolute Deviation (MAD) 0.0189
Skewness 0.13870964
Sum 25758804
Variance 0.00044948155
Monotonicity Not monotonic
2022-11-29T21:27:36.583510 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
144.946 746
 
0.4%
144.9455 690
 
0.4%
144.928 656
 
0.4%
144.9452 633
 
0.4%
144.9447 631
 
0.4%
144.9442 623
 
0.4%
144.9739 620
 
0.3%
144.9432 614
 
0.3%
144.9491 608
 
0.3%
144.9461 558
 
0.3%
Other values (5374) 171327
96.4%
Value Count Frequency (%)
144.9063 7
 
< 0.1%
144.90634 1
 
< 0.1%
144.9125 17
 
< 0.1%
144.91286 1
 
< 0.1%
144.9129 1
 
< 0.1%
144.9163 68
< 0.1%
144.9164 2
 
< 0.1%
144.91646 1
 
< 0.1%
144.9165 1
 
< 0.1%
144.9166 48
< 0.1%
Value Count Frequency (%)
144.9908 18
 
< 0.1%
144.99079 1
 
< 0.1%
144.9907 36
 
< 0.1%
144.99069 2
 
< 0.1%
144.99062 1
 
< 0.1%
144.9906 96
0.1%
144.99056 4
 
< 0.1%
144.99055 1
 
< 0.1%
144.99053 1
 
< 0.1%
144.9905 180
0.1%

y coordinate
Real number (ℝ)

Distinct 3820
Distinct (%) 2.1%
Missing 27
Missing (%) < 0.1%
Infinite 0
Infinite (%) 0.0%
Mean -37.802114
Minimum -37.8473
Maximum -37.7758
Zeros 0
Zeros (%) 0.0%
Negative 177706
Negative (%) > 99.9%
Memory size 1.4 MiB
2022-11-29T21:27:36.867489 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum -37.8473
5-th percentile -37.8352
Q1 -37.8063
median -37.7975
Q3 -37.7943
95-th percentile -37.7894
Maximum -37.7758
Range 0.0715
Interquartile range (IQR) 0.012

Descriptive statistics

Standard deviation 0.012825118
Coefficient of variation (CV) -0.00033926985
Kurtosis 1.6037765
Mean -37.802114
Median Absolute Deviation (MAD) 0.0048
Skewness -1.3701665
Sum -6717662.6
Variance 0.00016448364
Monotonicity Not monotonic
2022-11-29T21:27:37.020481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
-37.7953 1779
 
1.0%
-37.7949 1757
 
1.0%
-37.7964 1726
 
1.0%
-37.7954 1609
 
0.9%
-37.7951 1608
 
0.9%
-37.7958 1578
 
0.9%
-37.7955 1572
 
0.9%
-37.7962 1543
 
0.9%
-37.796 1517
 
0.9%
-37.7956 1496
 
0.8%
Other values (3810) 161521
90.9%
Value Count Frequency (%)
-37.8473 4
 
< 0.1%
-37.84634 1
 
< 0.1%
-37.8463 18
< 0.1%
-37.8456 12
 
< 0.1%
-37.84557 1
 
< 0.1%
-37.84531 1
 
< 0.1%
-37.8453 18
< 0.1%
-37.84503 1
 
< 0.1%
-37.845 31
< 0.1%
-37.84498 1
 
< 0.1%
Value Count Frequency (%)
-37.7758 10
 
< 0.1%
-37.77583 1
 
< 0.1%
-37.77586 1
 
< 0.1%
-37.7759 27
< 0.1%
-37.77595 1
 
< 0.1%
-37.77599 1
 
< 0.1%
-37.776 63
< 0.1%
-37.77601 1
 
< 0.1%
-37.77602 1
 
< 0.1%
-37.77606 1
 
< 0.1%

Location
Categorical

Distinct 19084
Distinct (%) 10.7%
Missing 27
Missing (%) < 0.1%
Memory size 1.4 MiB
POINT (144.9793 -37.8345)
 
355
POINT (144.9667 -37.8044)
 
181
POINT (144.9479 -37.7979)
 
170
POINT (144.9452 -37.799)
 
170
POINT (144.9694 -37.7948)
 
165
Other values (19079)
176665 

Length

Max length 46
Median length 25
Mean length 26.13977
Min length 21

Characters and Unicode

Total characters 4645194
Distinct characters 20
Distinct categories 7 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 10563 ?
Unique (%) 5.9%

Sample

1st row POINT (144.9567 -37.8199)
2nd row POINT (144.9558 -37.82)
3rd row POINT (144.9578 -37.82)
4th row POINT (144.9595 -37.8194)
5th row POINT (144.9577 -37.8195)

Common Values

Value Count Frequency (%)
POINT (144.9793 -37.8345) 355
 
0.2%
POINT (144.9667 -37.8044) 181
 
0.1%
POINT (144.9479 -37.7979) 170
 
0.1%
POINT (144.9452 -37.799) 170
 
0.1%
POINT (144.9694 -37.7948) 165
 
0.1%
POINT (144.946 -37.807) 162
 
0.1%
POINT (144.9522 -37.8096) 144
 
0.1%
POINT (144.9686 -37.7968) 123
 
0.1%
POINT (144.9856 -37.81) 108
 
0.1%
POINT (144.9744 -37.7973) 102
 
0.1%
Other values (19074) 176026
99.0%

Length

2022-11-29T21:27:37.171514 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
point 177706
33.3%
37.7953 1771
 
0.3%
37.7949 1742
 
0.3%
37.7964 1721
 
0.3%
37.7954 1601
 
0.3%
37.7951 1596
 
0.3%
37.7958 1571
 
0.3%
37.7955 1559
 
0.3%
37.7962 1532
 
0.3%
37.796 1511
 
0.3%
Other values (21649) 340808
63.9%

Most occurring characters

Value Count Frequency (%)
4 484809
 
10.4%
7 377297
 
8.1%
355412
 
7.7%
. 355412
 
7.7%
9 345200
 
7.4%
3 287769
 
6.2%
1 284167
 
6.1%
0 227765
 
4.9%
) 177706
 
3.8%
- 177706
 
3.8%
Other values (10) 1571951
33.8%

Most occurring categories

Value Count Frequency (%)
Decimal Number 2512722
54.1%
Uppercase Letter 888530
 
19.1%
Space Separator 355412
 
7.7%
Other Punctuation 355412
 
7.7%
Close Punctuation 177706
 
3.8%
Dash Punctuation 177706
 
3.8%
Open Punctuation 177706
 
3.8%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
4 484809
19.3%
7 377297
15.0%
9 345200
13.7%
3 287769
11.5%
1 284167
11.3%
0 227765
9.1%
8 175473
 
7.0%
2 115859
 
4.6%
5 111694
 
4.4%
6 102689
 
4.1%
Uppercase Letter
Value Count Frequency (%)
P 177706
20.0%
O 177706
20.0%
T 177706
20.0%
N 177706
20.0%
I 177706
20.0%
Space Separator
Value Count Frequency (%)
355412
100.0%
Other Punctuation
Value Count Frequency (%)
. 355412
100.0%
Close Punctuation
Value Count Frequency (%)
) 177706
100.0%
Dash Punctuation
Value Count Frequency (%)
- 177706
100.0%
Open Punctuation
Value Count Frequency (%)
( 177706
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 3756664
80.9%
Latin 888530
 
19.1%

Most frequent character per script

Common
Value Count Frequency (%)
4 484809
12.9%
7 377297
10.0%
355412
9.5%
. 355412
9.5%
9 345200
9.2%
3 287769
7.7%
1 284167
7.6%
0 227765
 
6.1%
) 177706
 
4.7%
- 177706
 
4.7%
Other values (5) 683421
18.2%
Latin
Value Count Frequency (%)
P 177706
20.0%
O 177706
20.0%
T 177706
20.0%
N 177706
20.0%
I 177706
20.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 4645194
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
4 484809
 
10.4%
7 377297
 
8.1%
355412
 
7.7%
. 355412
 
7.7%
9 345200
 
7.4%
3 287769
 
6.2%
1 284167
 
6.1%
0 227765
 
4.9%
) 177706
 
3.8%
- 177706
 
3.8%
Other values (10) 1571951
33.8%

Interactions

2022-11-29T21:27:31.826480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:23.596481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:25.080481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:26.517483 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:27.978480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:29.288479 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:30.536482 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:32.027510 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:23.870481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:25.262480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:26.733480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:28.180511 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:29.460480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:30.718512 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:32.212515 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:24.059480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:25.441580 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:26.914482 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:28.367480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:29.624480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:30.923482 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:32.414480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:24.247481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:25.741480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:27.115481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:28.558480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:29.812480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:31.104480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:32.607481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:24.429480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:25.920481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:27.305480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:28.754480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:29.993511 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:31.290481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:32.805480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:24.641482 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:26.125485 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:27.483480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:28.931480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:30.169480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:31.457480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:32.991482 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:24.846481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:26.324481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:27.662481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:29.106480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:30.339480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-11-29T21:27:31.635509 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-11-29T21:27:37.283482 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-29T21:27:37.472481 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-29T21:27:37.637509 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-29T21:27:37.803523 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-29T21:27:37.969482 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-29T21:27:38.095505 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-29T21:27:33.263511 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-29T21:27:33.768511 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-29T21:27:34.240483 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Census year Block ID Property ID Base property ID Building address CLUE small area Dwelling type Dwelling number x coordinate y coordinate Location
0 2002 11 103957 103957 517-537 Flinders Lane MELBOURNE 3000 Melbourne (CBD) Residential Apartments 6 144.9567 -37.8199 POINT (144.9567 -37.8199)
1 2002 11 559405 559405 547-555 Flinders Lane MELBOURNE 3000 Melbourne (CBD) Residential Apartments 91 144.9558 -37.8200 POINT (144.9558 -37.82)
2 2002 12 103995 103995 492-500 Flinders Street MELBOURNE 3000 Melbourne (CBD) Residential Apartments 114 144.9578 -37.8200 POINT (144.9578 -37.82)
3 2002 12 103999 103999 1-13 William Street MELBOURNE 3000 Melbourne (CBD) Residential Apartments 69 144.9595 -37.8194 POINT (144.9595 -37.8194)
4 2002 12 104823 104823 25-27 Highlander Lane MELBOURNE 3000 Melbourne (CBD) House/Townhouse 1 144.9577 -37.8195 POINT (144.9577 -37.8195)
5 2002 12 104825 104825 1-9 Highlander Lane MELBOURNE 3000 Melbourne (CBD) House/Townhouse 5 144.9581 -37.8199 POINT (144.9581 -37.8199)
6 2002 13 103954 103954 381-387 Flinders Lane MELBOURNE 3000 Melbourne (CBD) Residential Apartments 2 144.9614 -37.8183 POINT (144.9614 -37.8183)
7 2002 13 106435 106435 21-31 Market Street MELBOURNE 3000 Melbourne (CBD) Residential Apartments 111 144.9604 -37.8186 POINT (144.9604 -37.8186)
8 2002 14 103165 103165 1-5 Elizabeth Street MELBOURNE 3000 Melbourne (CBD) Residential Apartments 2 144.9646 -37.8181 POINT (144.9646 -37.8181)
9 2002 14 103168 103168 17-19 Elizabeth Street MELBOURNE 3000 Melbourne (CBD) Residential Apartments 3 144.9644 -37.8177 POINT (144.9644 -37.8177)
Census year Block ID Property ID Base property ID Building address CLUE small area Dwelling type Dwelling number x coordinate y coordinate Location
177723 2020 2547 615555 615555 79 Barnett Street KENSINGTON VIC 3031 Kensington House/Townhouse 1 144.93225 -37.79379 POINT (144.932249 -37.793788680000006)
177724 2020 2547 615557 615557 81 Barnett Street KENSINGTON VIC 3031 Kensington House/Townhouse 1 144.93224 -37.79384 POINT (144.9322403 -37.79383556)
177725 2020 2547 615559 615559 83 Barnett Street KENSINGTON VIC 3031 Kensington House/Townhouse 1 144.93223 -37.79388 POINT (144.9322312 -37.79388445)
177726 2020 2547 615560 615560 85 Barnett Street KENSINGTON VIC 3031 Kensington House/Townhouse 1 144.93222 -37.79394 POINT (144.9322215 -37.79393652)
177727 2020 2547 615562 615562 87 Barnett Street KENSINGTON VIC 3031 Kensington House/Townhouse 1 144.93221 -37.79399 POINT (144.9322116 -37.79398933)
177728 2020 2547 615563 615563 89 Barnett Street KENSINGTON VIC 3031 Kensington House/Townhouse 1 144.93220 -37.79404 POINT (144.9322016 -37.79404319)
177729 2020 2547 615570 615570 91 Barnett Street KENSINGTON VIC 3031 Kensington House/Townhouse 1 144.93219 -37.79410 POINT (144.9321916 -37.79409667)
177730 2020 2547 615573 615573 93 Barnett Street KENSINGTON VIC 3031 Kensington House/Townhouse 1 144.93218 -37.79415 POINT (144.9321816 -37.79415004)
177731 2020 2547 615577 615577 95 Barnett Street KENSINGTON VIC 3031 Kensington House/Townhouse 1 144.93217 -37.79420 POINT (144.9321718 -37.79420284)
177732 2020 2547 615579 615579 97 Barnett Street KENSINGTON VIC 3031 Kensington House/Townhouse 1 144.93216 -37.79425 POINT (144.9321628 -37.79425086)

Duplicate rows

Most frequently occurring

Census year Block ID Property ID Base property ID Building address CLUE small area Dwelling type Dwelling number x coordinate y coordinate Location # duplicates
0 2018 773 627016 627016 73-91 South Wharf Drive DOCKLANDS 3008 Docklands Residential Apartments 229 144.9360 -37.8224 POINT (144.936 -37.8224) 4
1 2019 773 627016 627016 73-91 South Wharf Drive DOCKLANDS 3008 Docklands Residential Apartments 229 144.9359 -37.8224 POINT (144.9359 -37.8224) 2
{% endblock %}